A virtual interview with Murray Aitkin by Brian Francis and John Hinde, two of the original members of the Centre for Applied Statistics that Murray created at Lancaster University. The talk ranges over Murray's reflections of a career in statistical modelling and the many different collaborations across the world that have been such a significant part of it.
AitkinM (1978) The analysis of unbalanced cross-classifications (with Discussion). Journal of the Royal Statistical Society A, 141, 195–223.
2.
AitkinM (1997) The calibration of P-values, posterior Bayes factors and the AIC from the posterior distribution of the likelihood (with Discussion). Statistics and Computing, 7, 253–72.
3.
AitkinM (2008) Applications of the Bayesian bootstrap finite population inference. Journal of Official Statistics, 24, 21–51.
AitkinM (2013a) Algebra and statistics. Amstat News, 436, 24–25.
6.
AitkinM (2013b) Comments on the review of Statistical Inference. Statistics and Risk Modeling, 30, 121–32.
7.
AitkinM (2018) A history of the GLIM statistical package. International Statistical Review, 86, 275–99.
8.
AitkinMAitkinI (2011) Statistical modeling of the national assessment of educational progress. New York, NY: Springer.
9.
AitkinMFoxallR (2003) Statistical modelling of artificial neural networks using the multilayer perceptron. Statistics and Computing, 13, 227–39.
10.
AitkinMHealeyAR (1984) Mathematical modelling of the EEC Labour Force Survey. In Recent Developments in the Analysis of Large-Scale Data Sets, pages 23–50. Luxembourg: Office for Official Publications of the European Communities.
11.
AitkinMHealeyAR (1987) Statistical modelling of the EEC Labour Force survey: A project history. In The statistical consultant in action, edited by DJ Hand and BS Everitt, pages 171–79. Cambridge: Cambridge University Press.
12.
AitkinMLiuCC (2018) Confidence, credibility and prediction (with discussion by Little and Welsh and response). Metron, 76, 305–20.
13.
AitkinMRocciR (2002) A general maximum likelihood analysis of measurement error in generalized linear models. Statistics and Computing, 12, 163–74.
14.
AitkinMStasinopoulosM (1989) Likelihood analysis of a binomial sample size problem. In Contributions to probability and statistics: Essays in honor of Ingram Olkin, edited by LJ Gleser, MD Perlman, SJ Press and AR Sampson, pages 399–411. New York, NY: Springer-Verlag.
15.
AitkinMAndersonDAHindeJP (1981) Statistical modelling of data on teaching styles (with Discussion). Journal of the Royal Statistical Society A, 144, 419–61.
16.
AitkinMAndersonDAFrancisBJHindeJP (1989) Statistical modelling in GLIM. Oxford: Clarendon Press.
17.
AitkinMBennettNHeskethJ (1981) Teaching styles and pupil progress: A reanalysis. British Journal of Educational Psychology, 51, 170–86.
18.
AitkinMBoysRJChadwickT (2005) Bayesian point null hypothesis testing via the posterior likelihood ratio. Statistics and Computing, 15, 217–30.
19.
AitkinMFrancisBJRaynalN (1987) Une e´tude comparative d’analyses des correspondances ou de classifications et des modeles de variables latentes ou de classes latentes. Revue de Statistique Applique´, 35, 53–82.
20.
AitkinMVuDFrancisB (2015) A new Bayesian approach for determining the number of components in a finite mixture. Metron, 73, 155–76.
21.
AitkinMVuDFrancisB (2017) Statistical modelling of a terrorist network. Journal of the Royal Statistical Society A, 180, 751–68.
22.
AlfòMAitkinM (2000) Random coefficient models for binary longitudinal responses with attrition. Statistics and Computing, 10, 275–83.
23.
BennettSN (1976) Teaching styles and pupil progress. London: Open Books.
24.
BockRDAitkinM (1981) Marginal maximum likelihood estimation of item parameters: An application of an EM algorithm. Psychometrika, 46, 443–59.
25.
DempsterAP (1974) The direct use of likelihood in significance testing. In Proceedings of the Conference on Foundational Questions in Statistical Inference, edited by O Barndorff-Nielsen, P. Blaesild and G. Schou, pages 335–52. Minneapolis, MN: University of Minnesota.
26.
DempsterAP (1997) The direct use of likelihood in significance testing. Statistics and Computing, 7, 247–52.
27.
DempsterALairdNRubinD (1977) Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society B, 39, 1–38.
28.
EricsonWA (1969) Subjective Bayesian models in sampling finite populations (with discussion). Journal of the Royal Statistical Society B, 31, 195–233.
29.
EvertonSF (2012) Disrupting dark networks. Cambridge: Cambridge University Press.
30.
FoxallR (2001) Statistical modelling of artificial neural networks. PhD thesis, University of Newcastle-upon-Tyne.
31.
HartleyHORaoJNS (1968) A new estimation theory for sample surveys. Biometrika, 55, 547–57.
32.
HodgesJLKrechDCrutchfieldRS (1975) Statlab: An empirical introduction to statistics. New York, NY: McGraw Hill.
33.
LiuCCAitkinM (2008) Bayes factors: Prior sensitivity and model generalizability. Journal of Mathematical Psychology, 52, 362–75.
34.
RubinD (1981) The Bayesian bootstrap. Annals of Statistics, 9, 130–34.
